AI Security Consulting

What shapes the cost of AI security work

Controls that enforce, not a policy that asks

Is AI security consulting the right fit?

Start here when

Consider another path when

Most AI work does not stall on the model. It stalls at the review, because nobody can answer what the thing is allowed to do, what it can read, what happens when someone tries to talk it into something, and who signs off when it acts. So the CISO says no, and the CIO inherits a pilot that works and cannot ship. We test the system the way an attacker would, then build the controls that let security approve it: scoped permissions, approval gates, logging, and a kill switch.

AI security consultants who red team your agents and assistants, then build the permissions, approval gates, logging, and kill switches that get them approved.

AI security consulting covers the testing and controls that make an AI system safe to deploy: red teaming for prompt injection and data leakage, scoping what an agent may read and do, approval gates on consequential actions, audit logging, and a way to shut it off. It is the work that turns a blocked pilot into an approved deployment.

AI security consulting tests an AI system the way an attacker would, then builds the controls that let it ship: scoped tool permissions, approval gates on consequential actions, audit logs, and a kill switch. Thinklytics does this so a security review becomes an approval rather than the reason a working pilot never reaches production.

Adversarial testing of your assistants and agents: prompt injection, data exfiltration through tool calls, unsafe actions, and jailbreak paths.

A permission model. Which agent may read which system, what it may write, the spend or scope limit on each, and the segregation of duties that stops one agent both raising and approving the same action.

Approval gates on anything consequential: customer-facing output, changes to a record of truth, and money movement.

Audit logging and a kill switch, so an incident is reconstructable and stoppable.

A penetration test of your network. This is about the AI layer and what it can reach.

A policy document. Written controls that nothing enforces are the problem, not the fix.

A compliance certificate. We make the system defensible, your auditor still audits it.

Threat assessment of each AI system in scope, written against how it is actually wired rather than how it was designed.

Red team findings with reproduction steps, ranked by what an attacker could actually get.

A review pack your security and compliance teams can sign, plus handover so your team can re-run the tests.

SOC 2 audit preparation time after access, lineage, and control evidence were made continuous rather than assembled per audit.

Audit deficiencies resolved by fixing ownership, access, and evidence at the data layer.

Annual compliance labor saved once control evidence stopped being gathered by hand.

Security will not approve the assistant and cannot say exactly what would change their mind.

There is no written scope for what it reads and does, so the review has nothing to assess and defaults to no.

The chatbot can be talked into revealing things it should not.

Retrieval is not filtered by the asking user's permissions, so the model can reach documents the person cannot.

Tool access was granted broadly during the pilot and never scoped down before it went live.

Prompts, retrievals, and tool calls are not logged, so there is no record of what the system saw or did.

It is the testing and control work that makes an AI system safe enough to deploy. Testing means probing the system the way an attacker would: prompt injection, pulling data out through tool calls, getting it to take actions it should not. Control work means scoping what the system may read and do, putting approval gates on consequential actions, logging everything, and keeping a way to switch it off.

It is getting a model to follow instructions that came from data rather than from you. If your assistant reads a document, and that document contains text telling the assistant to ignore its rules and email a file somewhere, a system without the right controls may do it. The defence is not better wording in the system prompt. It is limiting what the model can reach and requiring approval before it acts.

Our AI pilot is blocked by the security review. Can you unblock it?

That is the common case. Usually the review is stuck because nobody has written down what the system is allowed to do, so there is nothing concrete to approve or reject. We produce that scope, test against it, implement the missing controls, and hand security a pack they can actually sign.

Yes, because the risk is different. A chatbot that answers wrongly produces a bad answer. An agent that acts wrongly changes a record, sends a message, or moves money. For agents the testing concentrates on the tool boundary: what it can call, with what arguments, and what stops it.

A threat assessment and red team on one system is typically two to four weeks. Implementing the controls that come out of it is usually a further four to eight weeks, depending on how many systems the AI touches and how much of the permission model has to be built from nothing.

Partly, and they overlap. This work produces much of the evidence a regulator or auditor asks for: what the system does, what data it uses, what controls exist, and what is logged. For obligations specific to the EU AI Act, see our EU AI Act and AI Compliance Readiness service, which is scoped to that.

MLOps and drift monitoring watch whether a model is still accurate. They say nothing about what an agent is permitted to do. Agent governance is the other half: which agent may reach which system, what it may write, the spend limit on each, the approval gate before a consequential action, the action log, the segregation of duties that stops one agent raising and approving the same thing, and the kill switch. An accurate agent with unbounded permissions is still a liability.

Our agents touch Salesforce, SAP and ServiceNow. Does that change anything?

It raises the stakes rather than the difficulty. Those are records of truth, so a wrong action is not a bad answer, it is a changed customer record, a posted document, or an approved request. Agents reaching them need their permissions scoped per system, an approval gate on writes, and an action log that reconciles against the target system's own audit trail. We work inside your existing identity provider rather than adding a separate one.

Yes. The controls go into your identity provider, your logging stack, and your approval workflows rather than into a separate product. Your team re-runs the tests after we leave, so the handover includes the test suite and how to extend it.

Scope follows from a short assessment of what the AI actually touches. These are the factors that move the effort.

One internal assistant is a contained engagement. A fleet of agents across departments is not.

Read-only answering is a narrow test surface. Systems that write, send, or spend need far more guardrail work.

Each connected CRM, ticketing, or document store adds a permission boundary to test and enforce.

If permissions and audit logging already exist, we extend them. If not, that layer gets built first.

Regulated sectors need evidence assembled in a specific form, which adds documentation rather than engineering.

The assessment comes first, so the number reflects your actual exposure rather than a package price.

Most AI security deliverables are documents. A document does not stop an agent from calling a tool it should not.

A working AI pilot is blocked by a security or compliance review.

An assistant can reach documents some of its users should not see.

Agents can take actions and nobody can show what stops them.

You need evidence for an auditor about what the AI does and logs.

Your obligation is specifically EU AI Act conformity: see EU AI Act Compliance.

The gap is ownership and quality of the underlying data: see Data Governance Consulting.

You need someone to run and monitor the agents long term: see AI Governance and Managed Operations.

Agents built with approval gates and audit logs from the start.

Score the data and process before anything reaches production.